Features

• Schema oriented datasets builder

How to Use it

Terminal:

# Import the package into any python app import rawbuilder as rw

# Init the dataset object as ds ds = rw.DataSet( size=1000, task=’user’, )

# Build the dataset ds.build()

# Optionals ds = rw.DataSet( size=1000, task=’user’, schema_path=’where/to/read/schema/from’, schema_dict=’{‘user’:{‘id’:’int’}}’ )

df = ds.build( output_path=’your/output/directory’, export_csv=True, return_df=True )

Schema

• The Schema is a JSON object that describes three main components.

• The model names, the column names, and the data types per column.

• Note the below code-block, The model name is “Student”, and it contain 4 columns [id,first_name,email,math_test_results].

• Each property of the model “student” is called a task and it has its columns and data description.

Student data model example:

“student”: { “id”: “int”, “first_name”: “first_name”, “last_name”: “last_name”, “email”: “email”, “math_test_results”: “random_int between,0,30” }

Data Modifiers

Combine Data Modifiers to the above data types, it can adjust values, change the data nature, and gives more control over the final output.

Modifiers syntax is simple:

“modifier,argument_1,arg_2,arg_*”

Use the modifier between to generate random integer column between 0 and 30:

“math_test_results”: “random_int between,0,30”

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